Senior Machine Learning Engineer
Scientific Games Corporation19 days ago
Toronto, CanadaSenior
Responsibilities
- Build reusable self-service tooling for model packaging, deployment, batch inference, and real-time serving.
- Develop platform capabilities that allow Data Scientists to deploy, monitor, and iterate on production models independently.
- Create model registry, environment promotion, rollback, feature access, and inference API workflows.
- Design CI/CD pipelines for automated training, validation, shadow deployment, canary rollout, rollback, and production promotion.
- Establish golden-path templates, SDKs, CLIs, and reference implementations for ML system delivery.
- Contribute to observability standards covering model health, latency, feature freshness, data quality, and business KPIs.
- Partner with Staff Machine Learning Engineers, Data Scientists, and platform stakeholders on the first-generation ML platform architecture.
Requirements
- A master's degree in Computer Science, Engineering, Machine Learning, Software Engineering, or a related STEM field, or a related STEM bachelor's degree with strong equivalent industry depth.
- At least 3 years of hands-on experience in ML engineering, platform engineering, or production ML systems.
- Proven experience building production batch and real-time machine learning systems.
- Experience working with Data Scientists to productionize models and experimentation workflows.
- Strong experience building reusable tooling, frameworks, or internal developer platforms.
- Strong Python and software engineering fundamentals.
- Hands-on experience with PyTorch and TensorFlow model deployment workflows.
- Experience with Docker, Kubernetes, cloud-native deployment patterns, GitHub Actions, MLflow, model registries, and multi-environment promotion.
- Strong understanding of API-based inference services, asynchronous batch scoring, and event-driven pipelines.
- Experience building internal ML platforms from zero to scaled adoption, feature stores and reusable feature-access SDKs, Databricks, PySpark, Airflow, self-service experimentation, A/B testing, or platform abstractions is preferred.
Benefits
- The position starts remotely and transitions to a hybrid role.
- Candidates must be local to Toronto, Ontario.
Tech Stack
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